Embedding prior knowledge about measurement matrix into neural networks for compressed sensing Online publication date: Mon, 20-Apr-2020
by Meng Wang; Jing Yu; Chuangbai Xiao; Zhenhu Ning; Yang Cao
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 18, No. 3, 2020
Abstract: Different algorithms have been proposed for Compressed Sensing (CS). One of the most popular frameworks is Orthogonal Matching Pursuit (OMP). And there are many variants of it. Among various versions, a family of algorithms treats the distribution over an original signal as prior knowledge to obtain a training set for the model, and it achieves a good performance. However, there is other trivial prior knowledge about the measurement matrix that has never been used in compressed sensing in previous work. Hence, we propose a new method to embed the prior knowledge about the measurement matrix and distribution over the original signal into the neural networks for CS. In the end, the empirical support shows that the proposed method brings out a significant improvement.
Online publication date: Mon, 20-Apr-2020
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